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        <h2 id='online-health-evaluation'><span>Online Health Evaluation</span></h2>
        <p><span>Online health evaluation can predict a system’s health status and avoid dangerous accidents in advance. Health evaluation can be used for condition-based maintenance to ensure reliability and extend the whole life cycle of a system. According to the fault and understanding degree of flight control software, four models, namely fault tree model, stochastic hybrid dynamic system model, controllability model and data model, have been proposed to evaluate a system’s health.</span></p>
        <h3 id="stochastic-hybrid-dynamic-system-model12"><span>Stochastic hybrid dynamic system model <sup>[1],[2]</sup></span></h3>
        <p><span>Health performance prediction of a stochastic hybrid dynamic system aims at determining the probability or possibility that the system state will remain in a permitted area (safe set) or reach a forbidden area (unsafe set) at a future time instance.</span></p>
        <h3 id="fault-tree-model-34"><span>Fault tree model <sup>[3],[4]</sup> </span></h3>
        <p><span>Profust reliability theory extends the traditional binary state space into a fuzzy state space, which is therefore suitable to characterize a gradual physical degradation. Based on the fault tree model a profust reliability based health evaluation approach is proposed, where the profust reliability is employed as a health indicator to evaluate the real time system performance.</span></p>
        <h3 id="controllability-model-567"><span>Controllability model <sup>[5],[6],[7]</sup> </span></h3>
        <p><span>By taking the failure as a disturbance, the degree of controllability of the perturbation is taken as a health indicator.</span></p>
        <h3 id="data-model-8"><span>Data model <sup>[8]</sup> </span></h3>
        <p><span>A normal and abnormal system’s vibration signals are used to train an artificial neural network. Then, new data will feed into the trained artificial neural network to get a health indicator.</span></p>
        <p><strong><span>Papers</span></strong></p>
        <p><span>[1] Zhiyao Zhao, Quan Quan, Kai-Yuan Cai. A health performance prediction method of large-scale stochastic linear hybrid systems with small failure probability. Reliability Engineering and System Safety, 2017, 165: 74–88.</span></p>
        <p><span>[2] Zhiyao Zhao, Quan Quan, Kai-Yuan Cai. A health evaluation method of multicopters modeled by stochastic hybrid system. Aerospace Science and Technology 2017, 68: 149–162.</span></p>
        <p><span>[3] Zhiyao Zhao, Quan Quan, Kai-Yuan Cai. A profust reliability based approach to prognostics and health management. IEEE Transaction on Reliability, 2014, 63(1), 26-41.</span></p>
        <p><span>[4] Zhiyao Zhao, Quan Quan, Kai-Yuan Cai. A modified profust-performance-reliability algorithm and its application to dynamic systems. Journal of Intelligent and Fuzzy Systems 2017, 32(1): 643660.</span></p>
        <p><span>[5]Guang-Xun Du, Quan Quan, Binxian Yang and Kai-Yuan Cai. Controllability analysis for multirotor helicopter rotor degradation and failure. Journal of Guidance, Control, and Dynamics, 2015, 38(5): 978-985.doi: 10.2514/1.G000731</span></p>
        <p><span>[6]Guang-Xun Du, Quan Quan, Zhiyu Xi, Yang Liu, Kai-Yuan Cai. A Control Performance Index for Multicopters Under Off-nominal Conditions. Online: </span><a href="https://arxiv.org/abs/1705.08775" target="_blank" class="url">https://arxiv.org/abs/1705.08775</a><span>, [Video]</span></p>
        <p><span>[7] Guang-Xun Du, Quan Quan. Degree of Controllability and Its Application in Aircraft Flight Control. Journal of System Science and Mathematical Science, 2014 Vol. 34 (12): 1578-1594. (in Chinese)</span></p>
        <p><span>[8] Jiang Yan, Zhiyao Zhao, Haoxiang Liu, Quan Quan. Fault detection and identification for quadrotor based on airframe vibration signals: a data-driven method. Proceedings of the 34th Chinese Control Conference, July 28-30, 2015, Hangzhou, China</span></p>
        <h2 id="offline-performance-evaluation"><span>Offline Performance Evaluation</span></h2>
        <h3 id="basic-design-performance"><span>Basic Design Performance</span></h3>
        <p><span>In the design phase, designers and users wonder if an assembled multicopter can meet their performance requirements, such as hovering endurance, system efficiency, maximum load, maximum pitch, and maximum flight distance. However, in practice, they used to evaluate the performance of a multicopter through lots of flight experiments or by experience, which are normally inefficient and costly. We have proposed a comprehensive offline evaluation algorithm of ulticopter performance and launched a website </span><a href="http://www.flyeval.com" target="_blank" class="url">www.flyeval.com</a><span>. Reliability evaluation of different multicopter configurations is also proposed in [2].</span></p>
        <h3 id="degree-of-handling-performance"><span>Degree of Handling Performance</span></h3>
        <p><span>A multicopter is a kind of aircraft with simple structure and has the outstanding agility and handling performance. These made it wildly applicable in many fields and developed rapidly. In recent years, lots of functional multicopter products have been made. For different multicopter products, their flight performance and the difficulty degree of handling, which are called handling qualities, are different from each other. However, as far as we know, there is no existing methods and unified standards to assess multicopters so far. The handling quality assessment standards can offer a ‘ruler’ to compare for choosing multicopters, and it also can offer references to multicopter designers.</span></p>
        <p><strong><span>Papers</span></strong></p>
        <p><span>[1] Dongjie Shi, Xunhua Dai, Xiaowei Zhang, and Quan Quan. A practical performance evaluation method for electric multicopters. IEEE/ASME Transactions on Mechatronics. 2017, 22(3):13371348.</span></p>
        <p><span>[2] Dongjie Shi,Binxian Yang, Quan Quan. Reliability analysis of multicopter configurations based on controllability theory. Control Conference (CCC), 2016 35th Chinese. IEEE, 2016: 6740-6745.</span></p>
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